The ever-increasing universe of electronic information, for example as found on the World Wide Web (herein after referred to as the Web), competes for the effectively fixed and limited attention of people. Both consumers and producers of information want to understand what kinds of information are available, how desirable it is, and how its content and use change through time.
Making sense of very large collections of linked documents and foraging for information in such environments is difficult without specialized aids. Collections of linked documents are often connected together using hypertext links. The basic structure of linked hypertext is designed to promote the process of browsing from one document to another along hypertext links, which is unfortunately very slow and inefficient when hypertext collections become very large and heterogeneous. Two sorts of aids have evolved in such situations. The first are structures or tools that abstract and cluster information in some form of classification system. Examples of such would be library card catalogs and the Yahoo! Web site (URL http://www.yahoo.com). The second are systems that attempt to predict the information relevant to a user's needs and to order the presentation of information accordingly. Examples would include search engines such as Lycos (URL: http://www.lycos.com), which take a user's specifications of an information need, in the form of words and phrases, and return ranked lists of documents that are predicted to be relevant to the user's need.
Another system which provides aids in searching for information on the Web is the "Recommend" feature provided on the Alexa Internet Web site (URL: http://www.alexa.com). The "Recommend" feature provides a list of related Web pages that a user may want to retrieve and view based on the Web page that they are currently viewing.
It has been determined that one way to facilitate information seeking is through prediction of relevant web pages to a particular source page on a Web Site. One technique for such prediction is described by P. Pirolli, J. Pitkow and R. Rao in the publication entitled Silk from a Sow's Ear: Extracting Usable Structures from the Web, Conference on Human Factors in Computing Systems (CHI 96), Vancouver British Columbia, Canada, April 1996. Described therein is a "spreading activation" technique. Spreading activation techniques are based on representations of Web pages as nodes in graph networks representing usage, content, and hypertext relations among Web pages. Conceptually, activation is pumped into one or more of the graph networks at nodes representing some starting set of Web pages (i.e. focal points) and it flows through the arcs of the graph structure, with the amount of flow modulated by the arc strengths (which might also be thought of as arc flow capacities). The asymptotic pattern of activation over nodes will define the degree of predicted relevance of Web pages to the starting set of Web pages. By selecting the topmost active nodes or those above some set criterion value, Web pages may be aggregated and/or ranked based on their predicted relevance. It should be noted that the graph networks described are based on acquired information, i.e. the usage, content and hypertext, information for the Web site. Sometimes such acquired information, in particular the usage information, is difficult to obtain.
Another valuable piece of information would be estimates as to the number of "hits" a web page may be expected to have. This information would be very valuable to web site